1. Malarai Case Dataset Creation: Write a script to create a dataset that exclusively contains the number of malaria cases among males aged 6-11 months in Zambia, Zimbabwe, Mozambique and Malawi for the year 2021.
# read in the data 
test_data <- read.csv("data/test-data.csv")

# filter for relevant columns 
malaria_cases_2021 <- test_data %>%
  filter(
    cause_name == "Malaria",
    sex == "Male",
    age_group_name == "6-11 months",
    location_name %in% c("Zambia", "Zimbabwe", "Mozambique", "Malawi"),
    year == 2021,
    metric == "Number"
  ) %>%
  select(location_name, year, sex, age_group_name, cause_name, measure, metric, value, lower, upper)

print(malaria_cases_2021)
##   location_name year  sex age_group_name cause_name    measure metric     value
## 1        Malawi 2021 Male    6-11 months    Malaria prevalence Number 20882.284
## 2    Mozambique 2021 Male    6-11 months    Malaria prevalence Number 63252.395
## 3        Zambia 2021 Male    6-11 months    Malaria prevalence Number 17310.139
## 4      Zimbabwe 2021 Male    6-11 months    Malaria prevalence Number  1391.364
##       lower     upper
## 1 10536.170 38948.968
## 2 44321.640 85631.362
## 3  9891.434 25045.950
## 4  1024.114  1837.692
#write csv file of new dataset
write.csv(malaria_cases_2021, "data/malaria_cases_male_6_11_2021.csv")
  1. Trend Analysis Plot: Develop a script to create a plot that compares the trend of malaria cases from 2000 to 2021 among males aged 6-11 months in Zambia, Zimbabwe, Mozambique and Malawi
# filter data for malaria cases from 2000 to 2021 among males aged 6 - 11 months in Zambia, Zimbabwe, Mozambique and Malawi
malaria_cases_male_6_11_2000_2021 <- test_data %>%
  filter(
    cause_name == "Malaria",
    sex == "Male",
    age_group_name == "6-11 months",
    location_name %in% c("Zambia", "Zimbabwe", "Mozambique", "Malawi"),
    year >= 2000 & year <= 2021,
    metric == "Number"
  ) %>%
  select(location_name, year, sex, age_group_name, cause_name, measure, metric, value, lower, upper)

# create a trend analysis plot 
plot <- ggplot(malaria_cases_male_6_11_2000_2021, 
               aes(x = year, y = value, group = location_name)) +
  geom_ribbon(aes(ymin = lower, ymax = upper, fill = location_name), alpha = 0.2, color = NA) +
  geom_line(aes(color = location_name), linewidth = 1) +
  labs(
    title = "Trend of Malaria Cases in Males Aged 6–11 Months (2000–2021) for\nZambia, Zimbabwe, Mozambique, and Malawi",
    x = "Year",
    y = "Number of Malaria Cases",
    color = "Country",
    fill = "Country"  
  ) +
  theme(
    plot.title = element_text(size = 10),
    legend.title = element_text(size = 9),
    legend.text = element_text(size = 8)
  )

# static plot
plot

# show interactive plot
interactive_malaria <- ggplotly(plot)
interactive_malaria
  1. Malaria Prevalence Estimation: Using the population.csv and draws.csv data sets (draws are samples, or possible values of the statistic being estimated), to provide a single estimate of the prevalence of malaria in 2021 for males aged 6-11 months across Zambia, Zimbabwe, Mozambique, and Malawi, including 95% uncertainty intervals
# read in the data 
population <- read.csv("data/populations.csv")
draws <- read.csv("data/draws.csv")

# merge data sets 
merged <- draws %>%
  left_join(
    population %>% select(age_group_id, location_id, sex_id, year_id, population),
    by = c("age_group_id", "location_id", "sex_id", "year_id")
  )


# gets draws column 
draw_cols <- grep("^draw_" , names(merged), value = TRUE)

# calculate weighted prevalence for each draw 
weighted_prevalence <- sapply(draw_cols, function(draw_col) {
  sum(merged[[draw_col]] * merged$population, na.rm = TRUE) / sum(merged$population, na.rm = TRUE)
})

# summarize the weighted prevalence draws 
mean_prev <- mean(weighted_prevalence)
lower_95 <- quantile(weighted_prevalence, probs = 0.025)
upper_95 <- quantile(weighted_prevalence, probs = 0.975)

cat("Malaria prevalence of males aged 6-11 months during 2021 across Zambia, Zimbabwe, Mozambique, and Malawi):\n")
## Malaria prevalence of males aged 6-11 months during 2021 across Zambia, Zimbabwe, Mozambique, and Malawi):
cat(sprintf("Mean: %.4f\n95%% CI: %.4f - %.4f\n", mean_prev, lower_95, upper_95))
## Mean: 0.1546
## 95% CI: 0.1155 - 0.1980